Multimedia human-machine interface and interaction

Lips deliver visually active clues for speech articulation. Affective states define how humans articulate speech; hence, they also change articulation of lip motion. In this paper, we investigate effect of phonetic classes for affect recognition from lip articulations. The affect recognition problem is formalized in discrete activation, valence and dominance attributes. We use the symmetric Kullback-Leibler divergence (KLD) to rate phonetic classes with larger discrimination across different affective states. We perform experimental evaluations using the IEMOCAP database.

Natural and affective handshakes of two participants define the course of dyadic interaction. Affective states of the participants are expected to be correlated with the nature of the dyadic interaction. In this paper, we extract two classes of the dyadic interaction based on temporal clustering of affective states. We use the k-means temporal clustering to define the interaction classes, and utilize support vector machine based classifier to estimate the interaction class types from multimodal, speech and motion, features.

We propose a new algorithm for source localization on rigid surfaces, which allows one to convert daily objects into human-computer touch interfaces using surface-mounted vibration sensors. This is achieved via estimating the time-difference-of-arrivals (TDOA) of the signals across the sensors. In this work, we employ a smooth parametrized function to model the gradual noise-to-signal energy transition at each sensor. Specifically, the noise-to-signal transition is modeled by a four-parameter logistic function.

Negative symptoms in schizophrenia are associated with significant
burden and functional impairment, especially speech
production. There are no robust
treatments for negative symptoms and one obstacle surrounding
its research is the lack of an objective measure. To this
end, we explore non-verbal speech cues as objective measures.
Specifically, we extract these cues while schizophrenic
patients are interviewed by psychologists. Our results suggest a
strong correlation between certain measures of the two rating
sets.

Negative symptoms in schizophrenia are associated with significant
burden and functional impairment, especially speech
production. In clinical practice today, there are no robust
treatments for negative symptoms and one obstacle surrounding
its research is the lack of an objective measure. To this
end, we explore non-verbal speech cues as objective measures.
Specifically, we extract these cues while schizophrenic
patients are interviewed by psychologists. Our results suggest a
strong correlation between certain measures of the two rating